MicroscopeSketch: Accurate Sliding Estimation Using Adaptive Zooming
Yuhan Wu, Shiqi Jiang, Siyuan Dong, Zhong Zheng, Jiale Chen, Yutong Hu, Tong Yang, Steve Uhlig, Bin Cui
Abstract
High-accuracy real-time data stream estimations are critical for various applications, and sliding-window-based techniques have attracted wide attention. However, existing solutions struggle to achieve high accuracy, generality, and low memory usage simultaneously. To overcome these limitations, we present MicroscopeSketch, a high-accuracy sketch framework. Our key technique, called adaptive zooming, dynamically adjusts the granularity of counters to maximize accuracy while minimizing memory usage. By applying MicroscopeSketch to three specific tasks---frequency estimation, top-k frequent items discovery, and top-k heavy changes identification-we demonstrate substantial improvements over existing methods, reducing errors by roughly 4 times for frequency estimation and 3 times for identifying top-k items. The relevant source code is available in a GitHub repository.